Soil water repellency (SWR) is a property which has consequences for agricultural water management. The SWR is caused by hydrophobic organic coatings on mineral particles and the severity is highly depending on the organic matter quantity and quality and on the moisture status of the soil. It occurs mainly within a certain range of soil-water matric potential and is in this range varying dramatically with soil-water content. Recently, visible near-infrared spectroscopy (vis-NIR) has proven its applicability as an efficient method to determine texture, organic carbon (OC) and functional soil properties from one scanning. The vis-NIR spectral range between 400 and 2500 nm is dominated by broad absorption bands affected by OC quantity and quality, moisture content and texture. In this study, we applied vis-NIR spectroscopy to indirectly determine SWR. Since moisture can mask vis-NIR absorption features of OC and texture, the soil samples were vis-NIR scanned with a spectrometer (DS2500, Foss, Hillerød, Denmark) at three pre-treatments corresponding to three low soil water contents; air-dry and heat pre-treated at 60 and 105 °C, respectively. This study included 231 soil samples from New Zealand, Japan and Denmark exhibiting broad ranges in measured OC and clay contents. The degree of SWR was measured using the molarity of an ethanol droplet test on air-dry samples (SWR_AD), samples dried at 60 °C (SWR₆₀) and 105 °C (SWR₁₀₅), and samples exhibiting water contents above air-dry in small water content increments until the soil reaches a water content where it becomes hydrophilic. The actual water content was subsequently determined by oven-drying. Soil SWR was also evaluated by the integrated area under the SWR versus water content curve (SWR_AREA). The vis-NIR spectra were correlated to OC and SWR_AD, SWR₆₀, SWR₁₀₅ and SWR_AREA with partial least squares regression. Preliminary results yielded successful predictions of the OC and SWR_AD, SWR₆₀ and SWR₁₀₅. Furthermore, preliminary results for the applicability of vis-NIR to predict the SWR_AREA are promising.